1. Field of the Invention
The present invention relates generally to business performance targets and, more particularly, to generating revenue targets or other business performance targets.
2. Description of Related Art
Companies typically estimate expected revenues or generate revenue targets based on a number of factors. For example, one factor associated with estimating expected revenues is determining how much a customer is willing to spend for a particular product or service. The accuracy of these revenue expectations can have significant implications. For example, a company may alter its operating procedures and/or staffing level based on whether it meets its revenue expectation.
In practice, these revenue expectations are often derived in an ad hoc manner. For example, sales targets for a regional business division or sales targets to individual customers are typically set according to past performance or financial goals. Such sales targets, therefore, may reflect poor business or selling practices or may neglect intrinsic capabilities.
One conventional method used to estimate customer sales potential is regression analysis. Regression analysis generally estimates an average (or in some manifestations, an upper percentile) target. A problem with this approach is that the target, by definition, is not a maximal potential. Regression analysis also uses some pre-specified functional form and error structure to be applied to all customers at once. Such an approach is typically too restrictive for all different types of customers.
Other conventional methods used to estimate maximal or minimal targets are data envelopment analysis (DEA) and frontier analysis. In DEA analysis, the maximal target is described by φi=g(xi) where φi is the target for xi, a vector for the ith observation. In DEA, the task is to find a surface that exceeds or “envelopes” each observation. The estimated target is set to the maximum (or minimum) from the observed targets. One drawback with this approach is that it is sensitive to errors since it assumes that all observed targets define the possible space. As such, DEA is sensitive to outliers (i.e., observations that are far outside the other observations) and often results in unrealistic target values.
In frontier analysis, the target is described by φi=g(xi)+εi, where εi is a non-negative error term. This sets the target above its observed performance. One drawback with this approach is the requirement of a model for “g” and for the error term. Pre-specifying the functional form and the error term using some artificial mathematical model, e.g., linear, quadratic, Cobb-Douglass function, translog, etc. typically results in inadequate target values. In other words, relationships in the real world are not typically linear, quadratic, etc. Therefore, frontier analysis usually generates target values that not usable in real world scenarios.
Therefore, a need exists for systems and methods that enable a company to generate usable business performance targets.